{"id":"W2998795925","doi":"10.1109/twc.2019.2963185","title":"DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Noma; Computer science; Orthogonality; Deep learning; Single antenna interference cancellation; Task (project management); Channel (broadcasting); Computer engineering; Artificial intelligence; Distributed computing; Computer network; Telecommunications link","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001018165,0.0003418946,0.0003740667,0.0002147133,0.0009602344,0.00007027848,0.001800154,0.000305815,0.00002010565],"category_scores_gemma":[0.00006737197,0.0004162467,0.0001976783,0.0008438145,0.0002725617,0.0002910859,0.00002487188,0.001380935,0.00004747705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001919862,"about_ca_system_score_gemma":0.00003910641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001145339,"about_ca_topic_score_gemma":0.00006031786,"domain_scores_codex":[0.9984846,0.0001178277,0.000524786,0.00031621,0.0001550194,0.0004015383],"domain_scores_gemma":[0.996549,0.0009498126,0.0001280257,0.002070611,0.0001482072,0.0001543038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000025864,0.0001694348,0.00001885169,0.00006928284,0.0001278359,6.191469e-7,0.001236338,0.9195717,0.01339822,0.005240718,0.00001029012,0.06013086],"study_design_scores_gemma":[0.0006201905,0.00007214447,0.00001884219,0.00008163566,0.0000535141,0.000004014101,0.001261345,0.9742069,0.0202402,0.0006320687,0.002361225,0.0004479339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01001224,0.0007455031,0.9842775,0.001242059,0.0001473126,0.0006570779,0.0000571423,0.002763998,0.00009711021],"genre_scores_gemma":[0.6821876,0.001123757,0.3160464,0.000108962,0.00001562385,0.0003869646,0.00001930949,0.000100293,0.00001120244],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6721753,"threshold_uncertainty_score":0.9998289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06483600044087513,"score_gpt":0.2990843883546044,"score_spread":0.2342483879137293,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}